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import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys
import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive, plot_oni_index_th
from ind_setup.plotting import plot_base_map, plot_map_subplots
from ind_setup.core import fontsize
sys.path.append("../../../functions")
from data_downloaders import download_ERDDAP_data, download_oni_index
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[1], line 15
12 import matplotlib.pyplot as plt
14 sys.path.append("../../../../indicators_setup")
---> 15 from ind_setup.plotting_int import plot_timeseries_interactive, plot_oni_index_th
16 from ind_setup.plotting import plot_base_map, plot_map_subplots
17 from ind_setup.core import fontsize
ModuleNotFoundError: No module named 'ind_setup'
#Area of interest
lon_range = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]
shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"
data_xr = xr.load_dataset(op.join(path_data, 'sst_daily_1981_2024_palau.nc')).rename({'lat':'latitude', 'lon':'longitude'}).resample(time='1M').mean()
data_xr['time'] = data_xr.time.values.astype('datetime64[M]')
p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
df1 = download_oni_index(p_data)
lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)
data_xr['ONI'] = (('time'), df1.iloc[np.intersect1d(data_xr.time, df1.index, return_indices=True)[2]].ONI.values)
data_xr['ONI_cat'] = (('time'), np.where(data_xr.ONI < lims[0], -1, np.where(data_xr.ONI > lims[1], 1, 0)))
data_oni = data_xr.groupby('ONI_cat').mean()
data_oni['labels'] = (('ONI_cat'),['La Niña', 'Neutral', 'El Niño'])
dataset_id = 'sst'
plot_map_subplots(data_oni, dataset_id, shp_eez = shp_eez, cmap = 'hot_r',
vmin = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 0) - .25,
vmax = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 100) + .25,
sub_plot= [1, 3], figsize = (15, 8), cbar = True, cbar_pad = .1)
plt.savefig(op.join(path_figs, 'F14_SST_ENSO.png'), dpi=300, bbox_inches='tight')
data_an = data_oni - data_xr.mean(dim='time')
data_an['labels'] = (('ONI_cat'),['La Niña', 'Neutral', 'El Niño'])
plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.4, vmax=.4,
sub_plot= [1, 3], figsize = (15, 8), cbar = True, cbar_pad = 0.1)
